Technology of automated video observation of a drogue-sensor basket in the problem of autonomous aerial refueling

   The paper proposes a technology for automated video-based observation (VBO) of a drogue-sensor in the problem of aerial refueling. The technology is based on the use of a passive optoelectronic system and incorporates the logic of automated refueling observation of a refueling process using algorithms for the automatic detection and tracking of a drogue-sensor, a methodical apparatus for suboptimal linear filtering of the observed process under the conditions of spatial and temporary non-stationarity of the refueling process, algorithms for automatic correlation detection and tracking of a drogue-sensor using suboptimal filtering. An analysis of the design of experimental foreign systems for autonomous aerial refueling is carried out. The choice of the algorithm for the functioning of the synthetic vision system is substantiated. It is established that the main observation procedures: detection, capture for tracking and determination of the current drogue coordinates with a given rate and quality should be performed automatically, the pilot-operator takes part in the operation of the synthetic vision system in case of capture errors or mistracking. The statement of the problem for automated VBO of a drogue-sensor is formulated. A structural-logical diagram of the automated observation process, including the detection and tracking of a drogue, as well as decision-making by the pilot in various situations, is proposed. A modeling complex for a synthetic vision system operation is presented. The results of experimental studies of the synthetic vision system efficiency are presented. Based on the developed technology and the results of evaluating the effectiveness of automated observation algorithms, a strategy for performing autonomous refueling in conditions of various turbulence is proposed, while, during weak turbulence, a successful engagement is provided by tracking the center of drogue oscillations, in turn, under conditions of severe turbulence, a successful engagement can be provided by tracking a drogue controlled according to the synthetic vision system data.

1. Martinez, C., Richardson, T., Thomas, P., du Bois, J. & Campoy, P. (2013). A Vision-based strategy for autonomous aerial refueling tasks. Robotics and Autonomous Systems, vol. 61, issue 8, pp. 876–895. DOI: 10.1016/j.robot.2013.02.006

2. Bhandari, U., Thomas, P. R., Bullock, S., Richardson, T. S. & du Bois, J. L. (2013). Bow wave effect in probe and drogue aerial refueling. AAIA Guidance, Navigation and Control Conference, 19–22 August, pp. 1–21. DOI: 10.2514/6.2013-4695 (accessed: 18. 09. 2021).

3. Thomas, P. R., Bhandari, U., Bullock, S., Richardson, T. S. & du Bois, J. (2014). Advances in air to air refueling. Progress in Aerospace sciences, vol. 71, pp. 14–35. DOI: 10.1016/j.paerosci.2014.07.001

4. Thomas, P. R., Bullock, S., Richardson, T. S. & Whidborne, J. (2015). Collaborative control in a flying-boom aerial refueling simulation. Journal of Guidance, Control and Dynamics, vol. 38, no. 7, pp. 1–16. DOI: 10.2514/1.G000486 (accessed: 18. 09. 2021).

5. Fravolini, M. L., Campa, G. & Napolitano, M. R. (2007). Evaluation of machine vision algorithms for autonomous aerial refueling for unmanned aerial vehicles. Journal of Aerospace Computing, Information and Communication, vol. 4, no. 9, pp. 968–985. DOI: 10.2514/1.17269

6. Fravolini, M. L., Mammarella, M., Campa, G., Napolitano, M. R. & Perhinschi, M. (2010). Machine vision algorithms for autonomous aerial refueling for UAVs using the USAF refueling boom method. In book: Innovations in Defence Support Systems–1. Studies in Computational Intelligence, vol. 304, in: Finn A., Jain L. C. (ed.). Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-14084-6_5

7. Mammarella, M., Campa, G., Napolitano, M. R. & Fravolini, M. (2010). Comparison of point matching algorithms for the UAV aerial refueling problem. Machine Vision and Application, vol. 21, no. 3, pp. 241–251. DOI: 10.1007/s00138-008-0149-8

8. Campa, G., Napolitano, M. R. & Fravolini, M. (2009). Simulation environment for machine vision based aerial refueling for UAVs. IEEE Transactions on Aerospace and Electronic Systems, vol. 45, no. 1, pp. 138–151. DOI: 10.1109/TAES.2009.4805269

9. Vasilenko, G. I. (1977). [Holographic pattern recognition]. Moscow: Sovetskoye radio, 328 p. (in Russian)

10. Kolos, M. V. & Kolos, I. V. (2000). [Methods of optimal linear filtration], in Morozov V. A. (Ed.). Moscow: Izdatelstvo MGU, 102 p. (in Russian)

11. Gaidenkov, A. V. & Sharovatov, E. V. (2008). Investigation of fields ASF in task of optimum detection in scene of the signal of known form. Information-Measuring and Control Systems, vol. 6, no. 12, pp. 69–76. (in Russian)

12. Vyshinsky, V. V. & Kukushkin, L. S. (2011). Aerial refueling simulation in flight simulator with taking into account atmospheric and wake turbulence effect. Nauchnyy Vestnik MGTU GA, no. 172, pp. 34–41. (in Russian)

13. Didenko, V. P. & Tsitritsky, O. Ye. (1977). [Filtration and regularization]. Kyiv: KGU, 51 p. (in Russian)

14. Yaroslavsky, L. P. (1987). [Digital signal processing in optics and holography: an introduction to digital optics]. Moscow: Radio i svyaz, 296 p. (in Russian)

15. Astapov, Yu. M., Vasiliev, D. V. & Zalozhnev, Yu. I. (1988). [Theory of electrooptical tracking systems]. Moscow: Nauka, 328 p. (in Russian)

16. Gaidenkov, A. V. & Sharovatov, Ye. V. (2007). Investigation of the influence of image filtering parameters on the characteristics of the correlation detection of a signal of a known shape. Trudy VVIA. Aviatsionnoye radioelektronnoye oborudovaniye, no. 1, pp. 124–143. (in Russian)

17. Gruzman, I. S., Kirichuk, V. S., Kosykh, V. P., Peretyagin, G. I. & Spector, A. A. (2000). [Digital image processing in information systems]. Novosibirsk: Izdatelstvo NGTU, 168 p. (in Russian)

18. Gaidenkov, A. V. (2007). Synthesis and the analysis of fields of correlation functions in optics-electronic information systems. Information-Measuring and Control Systems, vol. 5, no. 2, pp. 3–7. (in Russian)

19. Gaidenkov, A. V. & Moskaltsov, A. N. (2007). Detection of the moving ground targets with low-contrast in conditions of turn and scaling of the image. Information-Measuring and Control Systems, vol. 5, no. 1, pp. 10–15. (in Russian)

20. Duda, R. O. & Hart, P. E. (1973). Pattern Classification and Scene Analysis. 1st ed. A Wiley-Interscience publication, 512 p.